Reference no: EM13882625
Part I. Construct training data files (ARFF files) using the training image data for three different bin numbers (i.e., number_of_bins = 8, 64 and 512). The number of training data files should be three.
Part II. Construct the five different classifier models using each training data file. The five classification methods are as follows:
1) Naïve Bayes Classifier
2) C4.5 Classifier
3) k-Nearest-Neighbor Classifiers
4) Multilayer Neural Network
5) Support Vector Classifier
Part III. Construct test data files (ARFF files) using the test image data per each category for three different bin numbers. The total number of test data files should be 21 (=7*3) in this case.
Part IV. Compare the prediction accuracies among five different classifiers for each category
Part V. Construct test data files (ARFF files) using all test image data for three different bin numbers. The total number of test data files should be three in this case. (You can easily construct this three test data files by combining the test data files constructed in Part III)
Part VI. Compare the prediction accuracies among five different classifiers for overall test data
- You CAN compute color histograms and construct ARFF files
a) Manually by using MS-Excel and/or any text editor (wordpad, textpad, etc)
b) Automatically by developing your own program with any programming language such as C, C++, Java, etc.
Part VII. Project Submission.
1. A project report (PDF file observe CSC573 presentation standards) describing
1. A comprehensive description of each classifier.
2. Accuracy comparison for each category preformed in Part IV
3. Accuracy comparison for overall test image preformed in Part VI
4. Your conclusions based on your observations